Face recognition can be performed by comparing two images to determine whether they show faces of the same person. For a given person whose identity is known, there may be a reference image of that person's face. In order to determine whether some new image (the “candidate” image) shows that same person's face, the face in the candidate image is compared with the face in the reference image. If the two faces have some threshold level of similarity, then the faces are deemed to be those of the same person.
A problem that arises in comparing images of faces is that images can vary widely in how they show the same person's face. Certain types of photos, such as passport photos or drivers license photos, are created to meet specific standards—e.g., the photos is a rectangle of a certain size, the head fits in a certain circle within that rectangle, the lighting meets certain parameters, and so on. It is relatively easy to compare facial features on photos that meet these kinds of exacting standards. However many photos are not taken to such exacting standards. For example, candid photos may capture a person's face at an oblique angle. Or lighting may be excessive or deficient. Or portions of the face may be occluded by objects or people in the photo. Comparing faces in these types of photos with a face in a reference image presents a challenge.
Various techniques exist to normalize the appearance of faces in order to facilitate comparison. For example, the face may undergo some sort of spatial alignment and/or lighting correction prior to comparison. However, even when such techniques are used, the same person's face may be significantly different in two photos. These differences may make it difficult to compare the faces, and to produce a reliable indication of whether the two faces are images of the same person.